Notes from the Video

• Matt Dancho, founder of Business Science
• Introduced to H2O-3 via the AutoML package
• Sample code in R shared
• Sample forecasting project / Walmart Sales
• Tidymodels standardize machine learning packages
• Modeltime loads H2O
• Multiple time series
• Create a forecast time horizon, assess 52 weeks forecast
• Create preprocessing steps, helps the H2O algos to find good features
• Some columns are normalized from the pre-processing
• Extracted Time related features (i.e. week number, day of the week, etc)
• Initializes H2O-3 / Stacked Ensemble model will be the best but hard to interpret
• Modeltime workflow starts with a table
• Modeltime is an organiational tool
• Modeltime Calibrate will extract the residuals of the models
• Visualize the forecast on the test set generates nice charts
• Built a single H2O-3 model to predict on 7 different time series
• This is very scalable, instead of looping through everything
• Refit the model on the entire training data and then did a forward walk of 52 weeks
• Modeltime ecosystem was created to help with higher frequent time series, at scale, that’s automated

• H2Oai
• AI Machine Learning